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Quantum data assimilation: a new approach to solving data assimilation on quantum annealers

Data assimilation is a crucial component in the Earth science field, enabling the integration of observation data with numerical models. In the context of numerical weather prediction (NWP), data assimilation is particularly vital for improving initial conditions and subsequent predictions. However, the computational demands imposed by conventional approaches, which employ iterative processes to minimize cost functions, pose notable challenges in computational time. The emergence of quantum computing provides promising opportunities to address these computation challenges by harnessing the inherent parallelism and optimization capabilities of quantum annealing machines.

Elevation-dependent warming: observations, models, and energetic mechanisms

Observational data and numerical models suggest that, under climate change, elevated land surfaces warm faster than non-elevated ones. Proposed drivers of this “elevation-dependent warming” (EDW) include surface albedo and water vapour feedbacks, the temperature dependence of longwave emission, and aerosols. Yet the relative importance of each proposed mechanism both regionally and at large scales is unclear, highlighting an incomplete physical understanding of EDW.

Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations

During the last 2 years, tremendous progress has been made in global data-driven weather models trained on reanalysis data. In this study, the Pangu-Weather model is compared to several numerical weather prediction models with and without probabilistic post-processing for temperature and wind speed forecasting. The results confirm that global data-driven models are promising for operational weather forecasting and that post-processing can improve these forecasts considerably.

Opinion: Optimizing climate models with process knowledge, resolution, and artificial intelligence

Climate models are crucial for predicting climate change in detail. This paper proposes a balanced approach to improving their accuracy by combining traditional process-based methods with modern artificial intelligence (AI) techniques while maximizing the resolution to allow for ensemble simulations. The authors propose using AI to learn from both observational and simulated data while incorporating existing physical knowledge to reduce data demands and improve climate prediction reliability.

A perspective on the next generation of Earth system model scenarios: towards representative emission pathways (REPs)

The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.

Opinion: Strengthening research in the Global South – atmospheric science opportunities in South America and Africa

To tackle the current pressing atmospheric science issues, as well as those in the future, a robust scientific community is necessary in all regions across the globe. Unfortunately, this does not yet exist. There are many geographical areas that are still underrepresented in the atmospheric science community, many of which are in the Global South. There are also larger gaps in the understanding of atmospheric composition, processes, and impacts in these regions. In this opinion, we focus on two geographical areas in the Global South to discuss some common challenges and constraints, with a focus on our strengths in atmospheric science research. It is these strengths, we believe, that highlight the critical role of Global South researchers in the future of atmospheric science research.

Synergistic approach of frozen hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework

In cloudy situations, infrared and microwave observations are complementary, with infrared being sensitive to cloud tops and microwave sensitive to precipitation. However, infrared satellite observations are underused. This study aims to quantify if the inconsistencies in the modelling of clouds prevent the use of cloudy infrared observations in the process of weather forecasting. It shows that the synergistic use of infrared and microwave observations is beneficial, despite inconsistencies.

On the importance of middle-atmosphere observations on ionospheric dynamics using WACCM-X and SAMI3

This study shows how middle-atmospheric data (starting at 40 km) affect day-to-day ionospheric variability. We do this by using lower atmospheric measurements that include and exclude the middle atmosphere in a coupled ionosphere–thermosphere model. Comparing the two simulations reveals differences in two thermosphere–ionosphere coupling mechanisms. Additionally, comparison against observations showed that including the middle-atmospheric data improved the resulting ionosphere.

Opinion: A research roadmap for exploring atmospheric methane removal via iron salt aerosol

Rapid reduction in atmospheric methane is needed to slow the rate of global warming. Reducing anthropogenic methane emissions is a top priority. However, atmospheric methane is also impacted by rising natural emissions and changing sinks. Studies of possible atmospheric methane removal approaches, such as iron salt aerosols to increase the chlorine radical sink, benefit from a roadmapped approach to understand if there may be viable and socially acceptable ways to decrease future risk.

Does high-latitude ionospheric electrodynamics exhibit hemispheric mirror symmetry?

In studies of the Earth’s ionosphere, a hot topic is how to estimate ionospheric conductivity. This is hard to do for a variety of reasons that mostly amount to a lack of measurements. In this study we use satellite measurements to estimate electromagnetic work and ionospheric conductances in both hemispheres. We identify where our model estimates are inconsistent with laws of physics, which partially solves a previous problem with unrealistic predictions of ionospheric conductances.

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